optimise_eu: Optimise Expected Utility
In bdpopt: Optimisation of Bayesian Decision Problems

Description Usage Arguments Details Value Author(s)

Optimisation of expected utility, either directly over the results of a grid evaluation performed by eval.on.grid or by optimisation of the regression function constructed by fit.gpr or fit.loess. This is a generic function for an S3 object.

1 2	optimise.eu(model, start, method = "L-BFGS-B", lower = -Inf, upper = Inf, control = list())

`model`	A simulation model object returned by `eval.on.grid`, `fit.gpr` or `fit.loess`. Specifying any method other than `"Grid"` requires that the object has been obtained from `fit.gpr` or `fit.loess`.
`start`	The start value when performing the search for a maximum. Passed on to `optim`.
`method`	The optimisation method to be used. Must be one of `"Nelder-Mead"`, `"BFGS"`, `"CG"`, `"L-BFGS-B"`, `"SANN"`, `"Brent"` or `"Grid"`.
`lower`	A numeric, atomic vector giving the lower limits for the decision variables when performing the maximisation.
`upper`	A numeric, atomic vector giving the upper limits for the decision variables when performing the maximisation.
`control`	A list of control parameters passed on to `optim`.

The optimisation strategy depends on the value of method. All arguments except model are ignored if the method "Grid" is used. If "L-BFGS-B" is used, then the arguments lower and upper are passed on as specified to optim as the lower and upper limits for the optimisation of the decision variables. If any other value is provided for method, then optim will be used to maximise a function defined to be equal to the objective function when the decision variable argument x satisfies x >= lower, x <= upper and equal to -Inf otherwise. The actual lower and upper limits passed to optim in this last case are -Inf and Inf, respectively.